ML Session n°3

ML Session n°3

2696500a913e29a26f38115f8ea56f71?s=128

Adrien Couque

March 08, 2017
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Transcript

  1. ML: regression March 2017

  2. Notations : Scalar (number) : Vector : Matrix : Transpose

    of matrix X : Mean of vectors x : Estimate of vector value x
  3. Simple linear regression

  4. Solving simple linear regression Goal : find the best and

    for Best ? Minimize the square residuals (least squares)
  5. Solving simple linear regression: finding the best estimates for a

    and b
  6. Demo 1

  7. Multivariate linear regression

  8. Demo 2

  9. Polynomial regression

  10. Trick : still a linear regression ! Just create additional

    columns, derived from pre-existing ones Then it comes back to a linear regression
  11. Demo 3

  12. Gradient descent

  13. Gradient descent

  14. Gradient descent

  15. Normal equation vs gradient descent Normal equation Gradient descent No

    additional parameters Need to choose a learning step No loop Needs to iterate : for inverse Slow if is large Works well when is large In practice : n < 10.000 ⇔ normal equation
  16. Logistic regression Used for binary classification Decision boundary : 0.5

    - y < 0.5 : class A - y >= 0.5 : class B
  17. Logistic regression : cost function

  18. Logistic regression : cost function

  19. Demo 4: logistic regression + gradient descent

  20. Questions? March 2017